Med Phys
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Volumetric modulated arc therapy (VMAT) optimization is a computationally challenging problem due to its large data size, high degrees of freedom, and many hardware constraints. High-performance graphics processing units (GPUs) have been used to speed up the computations. However, GPU's relatively small memory size cannot handle cases with a large dose-deposition coefficient (DDC) matrix in cases of, e.g., those with a large target size, multiple targets, multiple arcs, and/or small beamlet size. The main purpose of this paper is to report an implementation of a column-generation-based VMAT algorithm, previously developed in the authors' group, on a multi-GPU platform to solve the memory limitation problem. While the column-generation-based VMAT algorithm has been previously developed, the GPU implementation details have not been reported. Hence, another purpose is to present detailed techniques employed for GPU implementation. The authors also would like to utilize this particular problem as an example problem to study the feasibility of using a multi-GPU platform to solve large-scale problems in medical physics. ⋯ The results demonstrate that the multi-GPU implementation of the authors' column-generation-based VMAT optimization can handle the large-scale VMAT optimization problem efficiently without sacrificing plan quality. The authors' study may serve as an example to shed some light on other large-scale medical physics problems that require multi-GPU techniques.
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To check the accuracy of a gantry equipped with dual x-ray imagers and a robotic patient positioner for proton radiotherapy, and to evaluate the accuracy and feasibility of single-beam registration using the robotic positioner. ⋯ Results demonstrate that the gantry equipped with a robotic patient positioner and dual imaging panels satisfies treatment requirements for proton radiotherapy. The combined accuracy of the gantry, couch, and imagers allows a patient to be registered at one setup position and then moved precisely to another treatment position by commanding the robotic patient positioner and delivering treatment without requiring additional image registration.